Find Academic Supervisors

• Machine Learning
• Deep Learning
• Data Mining
• +3 more

Markus Goldstein is a Professor of Computer Science and Data Science at Ulm University of Applied Sciences (THU), where he also serves as Head of the Institute of Computer Science. His office is located in room Q229 at Albert-Einstein-Allee 53-55 in Ulm, Germany, and he can be reached at +49 731 96537-418 or Markus.Goldstein@thu.de. Professor Goldstein's primary research focuses on machine learning, deep learning, data mining, anomaly detection, and data science with noSQL databases. His work bridges theoretical foundations with practical applications, particularly in unsupervised anomaly detection methods and their implementation across various domains including security systems and big data analytics. His research program demonstrates consistent contributions to developing efficient algorithms for outlier detection and evaluation frameworks for comparing different approaches. His publication record shows a clear research trajectory focused on unsupervised anomaly detection, with recent emphasis on explainability in outlier detection systems (2023), comparative evaluations of algorithms (2016, 2015), and practical applications in security contexts (2013-2014). His work spans both theoretical contributions (such as FastLOF algorithm development in 2012) and applied research (including document authentication and DDoS mitigation). As an educator, Professor Goldstein teaches Programming in Java (I and II), Introduction to Computer Science, Databases, Machine Learning, Data Warehousing NoSQL and Big Data, and Advanced Machine Learning at the Master's level. He maintains regular office hours on Wednesdays at 2:00 PM with registration via email, and actively encourages student engagement through final papers and projects in his research areas.

Felix KiehnView profile

Researcher

• Polyglot Persistence
• Data Migration
• Distributed Systems
• +2 more

Felix Kiehn is a Researcher in the Department of Informatics at the University of Hamburg, where he focuses on distributed database systems and polyglot persistence architectures. He is currently completing his dissertation titled Design and Implementation of a scalable polyglot database system for data migration and mediation under the supervision of Prof. Dr. Norbert Ritter. His research explores: Polyglot persistence frameworks for heterogeneous data systems Data migration and mediation techniques Scalable architectures for NoSQL databases Distributed query optimization Kiehn's publications primarily address challenges in distributed data management, with recent work focusing on constraint modeling in polyglot systems (2024) and operator placement for spatio-temporal data processing (2022). He actively supervises student research, advising both Bachelor's and Master's theses on database topics. Current projects include contributing to the HADeS (Heterogenous and Adaptive Database System) research initiative.

Wolfram WingerathView profile

Researcher

• Real-Time Databases
• Stream Processing
• NoSQL Data Stores
• +5 more

Wolfram Wingerath is a Professor for Data Science at the University of Oldenburg (since 2022) and previously served as Head of Data Engineering & Research at Baqend GmbH (2018-2022). He earned his PhD in Computer Science (2019) from the University of Hamburg, focusing on scalable push-based real-time query systems (InvaliDB). His research interests include: Real-time databases and stream processing NoSQL and polystore systems Web performance optimization Probabilistic data management Cloud computing and distributed systems Key publication trends show expertise in real-time data architectures, caching mechanisms, and performance engineering. Notable collaborations include Benjamin Wollmer and Felix Gessert . He has advised 49+ student theses and served on PC committees for ICDE, VLDB, and EDBT. His editorial roles include membership on Datenbank-Spektrum and organizing the BTW 2025 Data Science Challenge .

Paul LibbrechtView profile

Professor

• Intelligent Learning Systems
• Web Architectures
• Mathematical Models
• +5 more

Paul Libbrecht is a Professor of Data and Computer Science at the online studies department of IU University of Applied Sciences. His expertise spans intelligent learning systems, web architectures, and mathematical models. He holds a PhD in Computer Science from the University of Saarland, focusing on learning materials for intelligent tutoring systems. Education: Bachelor's/Master's in Mathematics at University of Lausanne and Université du Québec à Montréal PhD in Computer Science, University of Saarland His research emphasizes infrastructure for research data, applied to learning analytics and web architectures. He contributed to foundational standards like MathML and the ActiveMath platform. His work bridges academia and industry, with roles at DFKI, Cabrilog, XWiki, and the Leibniz Institute. Grants & Advising: No explicit grants listed. No formal advisee names documented. Labs/Teams: Involved in development of ActiveMath platform and semantic web initiatives.

• Spatial data management
• Spatio-temporal statistics
• Machine learning in GIS
• +3 more

Felix Kunde is a Researcher at the Beuth University of Applied Sciences Berlin, contributing to the ExCELL and MAGDa projects. He holds a Bachelor's degree in Geography and a Master's degree in Geoinformation Science. His professional experience includes roles as a database developer for 3D city models and teaching geodatabase systems at universities. He actively participates in conferences such as FOSSGIS, FOSS4G, and WhereCamp, presenting work on topics like traffic forecasting, geospatial data management, and PostGIS extensions. Research interests focus on spatial data management (RDBMS/NoSQL/HDFS), spatio-temporal statistics, linear referencing, machine learning in GIS, and 3D city modeling. Current projects address short-term traffic forecasting with STARIMA/SVM/ANN methods, handling missing data in time series, and scalable geostatistical algorithms on Hadoop. He also works on open data models for traffic in OpenStreetMap and mobile databases using GeoPackage. Education: Bachelor of Geography Master of Geoinformation Science Key Contributions: Co-developer of pgMemento for spatial database auditing Contributor to the 3DCityDB project Lead researcher on ExCELL mobility platform Teaching: Lectures on geodatabases at multiple universities PostGIS workshops at conferences His publications span topics from geodatabase systems to traffic prediction algorithms, with notable work in spatial-temporal data analysis and geoinformatics applications. He advises multiple students on projects related to mobility analytics, data mining, and geospatial technology integration.

Lisa EhrlingerView profile

Researcher

• Data quality
• Knowledge graphs
• Ontologies
• +5 more

Lisa Ehrlinger is a Senior Researcher at the Information Systems Research Group within the Digital Engineering Faculty of the University of Potsdam. She holds a PhD in Engineering Sciences ("passed with distinction") and an MSc in Computer Science from Johannes Kepler University Linz (JKU), both with distinction. Her work bridges academic research and industrial application. Her research focuses on Data Quality , Knowledge Graphs , Ontologies , Semantic Technology , Data Catalogs , and Data Integration , particularly with NoSQL databases. She has over 7 years of applied research experience at the Software Competence Center Hagenberg GmbH (SCCH), including project management and team leadership. Her recent publications emphasize SHACL validation , Data Stream Pollution , Dashboard Design , and Ontology-Driven Analysis , reflecting trends in data quality automation , semantic modeling , and industrial data governance . She co-chaired the Quality of Databases (QDB) workshop (2023, 2024) and serves on the MIT CDOIQ Symposium review board (since 2023). Her outreach includes lecturing at JKU and reviewing for journals like ACM Journal of Data and Information Quality (JDIQ) and Elsevier Journal of Information Fusion .

• IoT and Sensor Networks
• Quantum Computing
• RFID Systems
• +4 more

Bartolomeo Montrucchio is a full Professor at the Polytechnic University of Turin's Department of Electrical, Computer and Biomedical Engineering. His research focuses on IoT systems, quantum computing applications, RFID technologies, and sensor networks. He leads interdisciplinary projects involving environmental monitoring, cybersecurity, and embedded systems design. Key research areas include: Development of low-cost air quality monitoring systems leveraging IoT and sensor fusion Quantum algorithm design for financial risk analysis and game theory applications RFID security protocols and anti-collision mechanisms for dense networks Energy-efficient image processing techniques for mobile displays Recent work highlights quantum computing's potential in vulnerability analysis and financial modeling, while maintaining strong contributions to sensor network reliability and environmental applications. His publications span prestigious journals like IEEE Internet of Things Journal and IEEE Transactions on Dependable and Secure Computing. Collaborations with industry partners and academic institutions emphasize practical implementation of theoretical advancements, particularly in smart city technologies and healthcare monitoring systems.

• XML Database Systems
• Cloud Computing Security
• Data Stream Mining
• +5 more

Eric Pardede is a professor at the School of Computing and Mathematics of Western Sydney University . He has an extensive publication record in data management, cybersecurity, and technology-enhanced learning, often collaborating with researchers like Wenny Rahayu, David Taniar, and A. S. M. Kayes. Research Focus: XML database systems, cloud computing security, data stream mining, and educational technology Award-Winning Contributions: Notable work on IoT security frameworks, blockchain risk assessment, and adaptive learning systems Recent Publications: 15 articles from 2021-2025 address topics like concept drift adaptation, cybersecurity in multi-cloud environments, and smart education tools Key Collaborations: Frequent partnerships with researchers in Australia, Indonesia, and Austria

Cedric du MouzaView profile

Professor

• Database Systems
• Data Mining
• Digital Humanities
• +6 more

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.

Samira Si-Said CherfiView profile

Professor

• Data Quality
• Conceptual Modeling
• Ontology Engineering
• +5 more

Samira Si-Said Cherfi is a Professor at the Centre d'études et de recherche en informatique et communications (CEDRIC) within the Conservatoire National des Arts et Métiers (CNAM) in Paris. With a research career spanning over 25 years, she has established herself as a leading expert in data quality, conceptual modeling, and ontology engineering. Her work bridges theoretical computer science with practical applications in healthcare systems, business process management, and knowledge representation. Her research interests focus on data quality assessment , conceptual modeling methodologies , ontology engineering for complex systems , and cyber-physical security in healthcare infrastructures . She has pioneered approaches for evaluating RDF data completeness, developing quality metrics for conceptual schemas, and creating ontologies for healthcare security. Her work demonstrates how formal modeling techniques can solve real-world problems in information systems. Analysis of her recent publications reveals a strong trend toward cyber-physical security and healthcare information systems , where she applies semantic technologies to address cascading effects in critical infrastructures. Her work consistently connects theoretical foundations in conceptual modeling with practical applications in knowledge graphs and data integration. She has made significant contributions to understanding how OWL semantics can be effectively utilized in RDF-based knowledge graphs. As an active member of the academic community, she has served as guest editor for special journal issues and contributed to major international conferences including RCIS, CAiSE, and EDOC. Her leadership in the field is evident through her editorial roles and collaborative research projects. Professor Si-Said Cherfi leads research within the CEDRIC laboratory, specifically contributing to the 'Complex data, machine learning and representations' and 'Data mining and statistics' research teams. Her work often involves interdisciplinary collaboration with healthcare professionals, security experts, and industry partners to address complex challenges in information systems security and data quality.

• Temporal Databases
• Time Series Analysis
• Database Systems
• +4 more

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .